Novel Time Domain Multi-class SVMs for Landmark Detection Rahul Chitturi, Mark Hasegawa Johnson*
University of Texas at Dallas, USA
rahul.ch@https://www.doczj.com/doc/f013746344.html,
* University of Illinois at Urbana Champaign, USA
jhasegaw@https://www.doczj.com/doc/f013746344.html,
Abstract
The training of precise speech recognition models depends on accurate segmentation of the phonemes in a training corpus. Segmentation is typically performed using HMMs, but recent speech recognition work suggests that the transient acoustic features characteristic of manner-class phoneme boundaries (landmarks) may be more precisely localized using acoustic classifiers specifically designed for the task of landmark detection. This paper makes an empirical exploration of new features which suit Landmark Detection and the application of Multi-class SVMs that are capable of improving the time alignment of phoneme boundaries proposed by Binary SVMs and HMM-based speech recognizer. On a standard benchmark data set (A database of Telugu - Official Indian Language, spoken by 75 million people), we achieve a new state-of-the-art performance, reducing RMS phone boundary alignment error from 32ms to 22ms.
Index Terms: Landmark, Multi Class SVM, Time Domain flatness measure, Segmentation
1Introduction
Accurately segmented training data improves the precision of both speech recognition and speech synthesis models. Phonetic segmentation has also been proposed as part of a lattice rescoring algorithm for multipass speech recognition [1]. Since manual segmentation of speech is time consuming and unrealistic in most conditions, various approaches on automatic speech segmentation have been proposed [2, 3], most typically including forced alignment of an HMM-based sentence model [4,5] observing standard speech recognition features such as energy, LPCC, MFCC, and PLP.
In 1998, Niyogi proposed the use of support vector machines (SVMs) to detect stop consonant bursts [6]. Instead of observing MFCCs once per 10ms, Niyogi's SVMs observed a much smaller feature vector (energy, lowpass energy, and spectral flatness) once per millisecond. Niyogi's SVMs significantly outperformed an HMM-based speech recognizer in this task [7]. Other authors have since used Niyogi's method to develop complete first-pass [8] and second-pass [1] speech recognition engines. As we proposed the time domain landmark detection techniques, we chose the features on the similar lines as Niyogi. We came up with a new feature which we named as Time Domain Flatness Measure gave the maximum performance for this problem.
Multi-class SVMs are usually implemented by combining several two-class SVMs. The one-versus-all method using winner-takes-all strategy and the one-versus-one method implemented by max-wins voting are popularly used for this purpose. In this paper we give empirical evidence to show that these methods are inferior to another one-versus-one method. The implementation of the binary SVMs was normal one-versus-one SVMs and so it is not explained in detail due to space constraints.
2Related work
The usage of Multiclass SVMs in speech was introduced in 2002, by Salomon et al. for phoneme classification [9]. Recently in 2005, Yin An-ron et al. used this Multi Class SVM as an extension of binary classifier using ECOC (Hadamard Error-Correcting Output Code [10]. Jorge Bernal-Chaves et al. used the Multi class SVMs for isolated word recognition [11]. In 2006, Andrew Hatch et al. have used the same technique for Speaker Recognition [12]
In this article, we extend the usage of Multi-class SVMs for Phoneme segmentation with HMM segmentation system as the baseline. We also explore new features which suit Landmark Detection.
3Database Description
All our experiments were conducted on the Telugu (Official Indian Language- spoken by 75 million people) database, which has over 600 sentences collected from a native speaker and contains substantial coarticulatory effects. This database was collected at a 16 KHz sampling frequency and a single channel, using a headset. This database has well defined transcriptions corresponding to the speech data and even the phone-level alignment was done manually. For the purpose of exact evaluation of our algorithms, we used the manual phone-alignments, but we don’t assume that these are necessary.
4Baseline System
The goal of the algorithms proposed in this paper is to refine the phoneme boundary times proposed by the HMM, in order to reduce their RMS error with reference to a “ground truth” manual phonetic transcription. Though HMMs are known to work better for tri-phone models, since we are concerned only with the time boundaries of a particular phone, monophone based HMM segmentation system is
done. The 39-element feature vector includes 12 MFCCs plus sum-squared signal energy, with delta and delta-delta coefficients appended. There are a total of 48 phones as shown in Fig. 1. Each phone is modeled by 3 emitting states, with 3 Gaussians per state. HMMs were initialized flat (to the same mean and variance), then HMMs were trained using embedded re-estimation for 15 iterations. Finally, forced alignment was done to get the phone boundaries. This system achieved an RMS phone boundary alignment
error of 32ms
Figure 1- Phones in Telugu
5Multi-class SVMs
In this section, we briefly review the implementations of all multiclass methods that will be studied in this paper. For a given multiclass problem, M will denote the number of classes and ωi, i=1,…, M. will denote the M classes. If the output of each binary classifier can be interpreted as the posterior probability of the positive class, Hastie and Tibshirani [13] suggested a pairwise coupling strategy for combining the probabilistic outputs of all the one-versus-one binary classifiers to obtain estimates of the posterior probabilities p i=Prob (ωi |x), i=1… M. After these are estimated, the PWC strategy assigns the example under consideration to the class with the largest p i.
The actual problem formulation and procedure for doing this are as follows. The binary classifier Cij is trained taking the examples from ωi as positive and the examples from ωi as negative. Let us denote the probabilistic output of C ij as r ij = Prob(ωi | ωi orωj). To estimate the p i’s, M(M-1)/2 auxiliary variables μij’s which relate to the p i’s are introduced:μij= p i / (p i + p j). p i’s are then determined so that μij’s are close to r ij’s in some sense. The Kullback-Leibler distance between r ij and μij is chosen as the measurement of
ij i j
The associated score equations are:
M
i
r
n
n
i
j i
j
ij
ij
ij
ij
,..,
1
,=
=
∑∑
≠≠
μSubject to1
1
=
∑
=
M
k
k
p (2)
The p i‘s are computed using the following iterative procedure 1.
~pi’s is identical to that based on the p i‘s obtained from pairwise coupling. However, ~pi’s are inferior to the p i‘s as estimates of posteriori probabilities. Also, log-likelihood values play an important role in the tuning of hyper parameters. So, it is always better to use the p i‘s as estimates of posteriori probabilities. Kernel logistic regression (KLR) has a direct probabilistic interpretation built into its model and its output is the positive class posterior probability. Thus KLR can be directly used as the binary classification method in the PWC implementation. We will refer to this multiclass method as PWC KLR.
Procedure 1
The output of an SVM, however, is not a probabilistic value, but an un-calibrated distance measurement of an example to the separating hyperplane in the feature space. Platt [14] proposed a method to map the output of an SVM into the positive class posterior probability by applying a sigmoid
where f is the output of the SVM associated with example x. The parameters A and B can be determined by minimizing the negative log-likelihood (NLL) function of the validation data. A pseudo-code for determining A and B is also given in [14]; To distinguish from the usual SVM, we refer to the combination of SVM together with the sigmoid function mentioned above as PSVM. The multiclass method that uses Platt’s probabilities together with PWC strategy will be referred to as PWC PSVM.
6Phone Boundary Refinement using SVMs
Landmarks are the boundaries between phones of different manner class. For refining the landmarks, the phonetic feature theory provides a hierarchical framework [6] and support vector machines (SVMs) provide the methodology for combining the speech knowledge [15]. The success of SVMs has been demonstrated for the problem of detection of stop consonants [7]. This article proposes the
use of heterogeneous classifiers, including SVMs, in a second-pass speech segmentation system. First segmentation
is done using HMM techniques and then the segmented data
is sent to the phone boundary refiner.
6.1Refinement Using Multi-Class SVMs
Previous landmark-based speech recognition systems [7, 8, 1] used SVMs as a kind of nonlinear filter. In these nonlinear-filtering methods, an SVM is applied sequentially to each frame of the input speech, with context taken into account using input memory buffers; if the SVM output exceeds a threshold in any frame, that frame is marked as a potential landmark. The nonlinear-filter application of SVMs was compared to that of a novel multi-class SVM-based phone boundary refinement algorithm, described in the remainder of this section. In all experimental tests, the multi-class SVM-based refinement algorithm outperformed the filter-style landmark detection algorithm.
Figure 2 shows a sample window which we use for extracting the features and the classes of the SVMs. The frame number in which the manually labeled phone boundary falls with respect to the HMM-proposed boundary is considered to be the target class. This can be understood by looking at the following sample window. In this, the target class for the given sample is 9-6 = 3. The feature vector is the vector of energies of the frames in the window, i.e., 8 frames before and 4 frames after the boundary proposed by the HMM. Frames for SVM analysis were 5ms
in length.
Figure 2- Sample window
We use the procedure2 for extracting the features and the corresponding classes.
Procedure 2
where, f ik is the frame number in which the manually labeled boundary falls with respect to the HMM boundary, C i is the target class label of SVM for the token l i and F.V i is the feature vector for the token l i . Using these feature vectors and the corresponding class labels we can train the SVMs.
6.2Acoustic Features Observed by the SVM
In order to get high accuracy for classification and segmentation, good features need to be selected that can model the temporal and spectral structures of audio [16]. In this paper we first test standard speech recognition features such as short time energy and MFCC. In addition to the standard features, various new features like spectral flatness and time domain flatness measures are analyzed and tested for SVM-based phone boundary refinement.
6.2.1Bandpass dE/dT
The energy of a speech signal passed through a number of different bandpass filters can be used to distinguish different manner classes [17]. We tested, as features, the time-domain derivatives of the following energies:
0- 400 Hz: useful to detect voicing
300 -1000 Hz: useful to detect sonority
1000 - 3000 Hz: distinguish nasals from glides
2000 -6000 Hz: detect frication energy
Full Band (no filtering): detect stop closures
6.2.2Spectral Flatness Measure
Temporal changes in the spectra of speech signals are believed to play an important role in human perception. Useful spectral shape information can be derived from very short temporal windows using the spectral flatness measure. Spectral flatness is calculated by the following formula [7]: ∫∫
?t)df)
S(f,
(
t))df
(S(f,log
log (4)
6.2.3Time Domain Flatness (TDF) Measure
In analogy to Niyogi's spectral flatness measure, we define a “time domain flatness” measure. Energy as a function of time is flat during a quasi-static region, and non-flat at a landmark. Our intuitive notion of “time domain flatness” can be formalized as follows:
∫∫
?E(t)dt)
(
(E(t))dt log
log (5)
where the integral is approximated by a sum over 7 sequential frames. Fig. 3 shows the plot of the flatness measure. Most of the actual landmarks are near the local minima of the TDF measure.
7Experimental Results
This section compares the features and algorithms described in the previous sections. These experiments were conducted on the database that is described in Sec. 3. Multi-class phone boundary refinement SVMs were trained and tested using, individually and in combination, the features described in Sec. 6.2. Results are shown in Table 1. The second column represents the percentage of phones that have alignment error less than 5 ms. and similarly 10ms in the third column. Similarly in Table 2 we have different bands of deviation and the corresponding performance of algorithms. The most accurate SVM shows the Time Domain Flatness feature as the best feature.
Since the results vary with the speech database, and as there is no benchmark speech database for the Telugu Language, we have only compared our algorithms with the methods that are usually employed like HMMs, on the database that was described in Section 2. The train data set has nearly 500 sentences and the test data set has 100
man HMM
| |
F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12 F13 | |
for all HMM phone boundaries l i in the signal
C i = f ik
for all the frames f i j of 5 ms in the
window of ( l i – 40)ms to (l i+20) ms
F.V ij = energy(f i j)
end
end
sentences, each sentence having approximately 40-50 phonemes (10-15 words).
Figure 3- Plot of Time Domain Flatness measure Feature < 5ms <10 ms
Individual Bandpass dE/dt 21% 31% Combined Bandpass dE/dt 19% 30% Spectral Flatness 18.5% 34%
Time Domain Flatness 24% 36%
Time domain Flatness+
Bandpass Energies
19% 28%
Table 1: Percentage of phone boundary alignment errors below threshold, as a function of the acoustic feature observed using Multi-class SVMs.
Deviation % of phones using
Multi-SVM Bin-SVM HMM
<5ms 24% 8% 3.82% <10ms 36.3% 11.2% 8.7%
<15ms 47.2% 21% 18%
<20ms 55.8% 38% 34.3% Average 22 ms 26.1 ms 32 ms
below threshold with Multi class SVMs.
8References
[1] M. Hasegawa-Johnson et al Landmark-Based Speech
Recognition: Report of the 2004 Johns Hopkins Summer
Workshop. Technical report [2] Y.-J. Kim, A. Conkie, “Automatic Segmentation
Combining an HMM-Based Approach and Spectral
Boundary Correction,” in: Proc. ICSLP2002, Denver,
Colorado, 145- 148, Sept. 2002.
[3] Syrdal, AK, Hirschberg, J., McGory, J. and Beckman,
M., "Automatic ToBI prediction and alignment to speed
manual labeling of prosody," In Speech. Communication,
vol. 33, 135-151.
[4] K. Tokuda, H. Zen, A.W. Black, ``An HMM-based
speech synthesis system applied to English,'' IEEE
Speech Synthesis Workshop, 2002.
[5] A. Sethy and S. Narayanan, “Refined speech
segmentation for concatenative synthesis.” ICSLP, 2002. [6] P. Niyogi, “Distinctive Feature Detection Using Support
Vector Machines.'' ICASSP pp 425-428, 1998.
[7] P. Niyogi, C. Burges, P. Ramesh, ``Distinctive Feature
Detection using Support Vector Machines,'' Proc.
ICASSP, 1999
[8] A. Juneja and C. Espy-Wilson, "Speech segmentation
using probabilistic phonetic feature hierarchy and support
vector machines." Proc. International Joint Conference
on Neural Networks, 2003
[9] Jesper Salomon, Simon King FRAMEWISE PHONE
CLASSIFICATION USING SUPPORT VECTOR MACHINES
– ICASSP 2002
[10]Yin An-rong, Xie Xiang, Kuang Jing-ming- Using Hadamard ECOC in multi-class problems based on SVM
- Interspeech 2005
[11] Jorge Bernal-Chaveset al Multiclass SVM-Based isolated-digit recognition using a HMM-guided segmentation- NOLISP 2005
[12] Andrew Hatch et al Generalized Linear Kernels for
One-Versus-All Classification: Application to Speaker
Recognition –ICASSP 2006
[13] Hastie, T., Tibshirani, R. (1998) Classification by
pairwise coupling. In Jordan, M.I., Kearns, M.J., Solla,
A.S. (eds.), Advances in Neural Information Processing
Systems, Vol. 10.
[14] Platt, J. (1999) Probabilistic outputs for support vector
machines and comparison to regularized likelihood
methods. In Smola, A.J., Bartlett, P., Sch¨olkopf, B.,
Schuurmans, D. (eds.), Advances in Large Margin
Classifiers, pp. 61–74. MIT Press.
[15] P.Niyogi and M. M. Sondhi. “Detecting Stop
Consonants in Continuous Speech” Journal of the
Acoustical Society of America. 111, 1063 (2002)
[16] E. Scheirer and M. Slaney, “Construction and
Evaluation of a Robust Multifeature Music/Speech
Discriminator.” Proc ICASSP pp 1331-1334, 1997
[17] S. Liu, “Landmark detection for distinctive-feature
based speech recognition.” J. Acoustical Society of
America100(5):3417-3430, 1996
WHEN与WHILE用法区别 when, while这三个词都有"当……时候"之意，但用法有所不同，使用时要特别注意。 ①when意为"在……时刻或时期"，它可兼指"时间点"与"时间段"，所引导的从句的动词既可以是终止性动词，也可是持续性动词。如： When I got home, he was having supper.我到家时，他正在吃饭。 When I was young, I liked dancing.我年轻时喜欢跳舞。 ②while只指"时间段"，不指"时间点"，从句的动词只限于持续性动词。如：While I slept, a thief broke in.在我睡觉时,盗贼闯了进来。 辨析 ①when从句与主句动作先后发生时，不能与while互换。如： When he has finished his work, he takes a short rest.每当他做完工作后，总要稍稍休息一下。(when = after) When I got to the cinema, the film had already begun.当我到电影院时，电影已经开始了。(when=before) ②when从句动词为终止性动词时，不能由while替换。如： When he came yesterday, we were playing basketball.昨天他来时，我们正在打篮球。 ③当从句的谓语是表动作的延续性动词时，when, while才有可能互相替代。如：While / When we were still laughing, the teacher came in.正当我们仍在大声嬉笑时，老师进来了。 ④当从句的谓语动词是终止性动词，而且主句的谓语动词也是终止性动词 时,when可和as通用，而且用as比用when在时间上更为紧凑，有"正当这时"的含义。如： He came just as (or when) I reached the door.我刚到门那儿，他就来了。 ⑤从句的谓语动词如表示状态时，通常用while。如： We must strike while the iron is hot.我们应该趁热打铁。 ⑥while和when都可以用作并列连词。
CHAPTER ONE accidentally意外的，偶然的 act行动;扮演（角色）atmosphere大气层；气氛 author作者 billion（英德）万亿；（美法）十亿burn燃烧 CFC氟利昂 burn up烧得更旺，烧毁 carbon dioxide 二氧化碳 compare比较，比作 consumer消费者 green consumer环保消费者 cover涉及，包括 damage损害，损毁 destruction破坏，毁坏 dioxide二氧化物 do with处理 drown溺死 erosion侵蚀，腐蚀 flood淹没，洪水 flooding洪水泛滥 fuel燃料 greenhouse温室 layer层 Greenhouse Effect温室效应 keep in把....关在..里面 level 水平高度，级别 lifeless无生命的 malaria疟疾 massive巨大的，大量的 nitrogen氮 occur出现，存在，发生 ozone臭氧 petrol汽油 protective保护的 rain forest（热带）雨林 spray can喷雾罐 substance 物质 take in吸入，摄入 threat威胁 vital与生命有关的，极重要的warmth温暖 wrecked毁坏的 durian榴莲 leaded含铅的 motor机动的 solar-powered以太阳能为动力的unleaded不含铅的 canopy罩棚，覆盖物 ect. 等等 exhaust排出的废气 fume（难闻的）气 incinerator焚化炉 inform通知 inquire询问 classical古典的 comedy喜剧 preference偏爱 respond回答 club sandwich总会三明治CHAPTER TWO actually 实际上 aim目的 airline航空公司 Auckland奥克兰 Brazil巴西 change找头，零钱 check-out收银台 climate 气候 confident自信的 deeply深深地，极大地，强烈的dump（垃圾）堆 educational教育的 fence围栏，栅栏 flower-arranging插花 flyover立交桥 foreign外国的 friendship友谊 fund（为机构.项目等）拨款gap分离，差距 golf高尔夫球运动 hoot鸣响（喇叭，汽笛等）host（待客的）主人
一、从句是如何出题的？ 1. 时态 2. 考连接词 3. 考语言顺序 二、学好从句的两个基本条件 1. 时态 2. 从句的三个必须:①必须是句子；②必须有连接词；③必须是陈述句 三、状语从句、宾语从句、定语从句重点 1.如何判断何种从句 2. 从句的时态 3. 从句的连接词与扩展 4. 经典单选、从句与选词、长句子分析 四、如何判断三种从句 1. 状语从句无先行词 2. 宾语(表语)从句无先行词有动词或词组 3. 定语从句先行词多为名词或代词 一、When引导的定语从句与时间状语从句的区分 1. when的译法不同。在时间状语中，when 翻译成“当……的时候” I want to be a teacher when I grow up. 当我长大的时候，我要做一名老师。在定语从句中，when不翻译。I won't forget the day when he says he loves me. 我不会忘记他说爱我的那一天。 2. 在时间状语中，when从句前面或后面是句子；定语从句中，when 从句不能位于句首，且通常when前为表示时间的名词day、year等。 3. when在从句的作用不同。在时间状语从句中，when是连词，只起连接主句和从句的作用，不做从句的任何成分。不过when引导的时间状语从句修饰主句的谓语，做主句的时间状语。 在定语从句中，when是关系副词，在从句中代替先行词做从句的时间状语，修饰从句的谓语。 例1 I will always remember the days when I lived with my
grandparents in the country. 例2 I always remember the days in the country when I see the photo of my grandparents. 点评：例1意为“我会永远记得跟我祖父母一起住在乡下的那些日子”，其中when 引导的是一个定语从句, 修饰the days, when在从句中作时间状语。例2意为“当我看到祖父母的照片时，总是会想起在乡下的那些日子”，其中when 引导的从句并不修饰前面的名词the country，因此可判定为时间状语从句。 例1中的when可用in which替代，即从句可改为...in which I lived with my grandparents in the country. 例2中从句前有名词，但根据句意可 知并不是从句所修饰的对象，也不能用“介词+ which”来替代。 二、判断关系代词与关系副词 方法一：用关系代词，还是关系副词完全取决于从句中的谓语动词。及物动词后面无宾语，就必须要求用关系代词；而不及物动词则要求用关系副词。例如： This is the mountain village where I stayed last year. 这是我去年呆过的山村。 I'll never forget the days when I worked together with you. 我永远不会忘记与你共事的日子。 判断改错： 1. This is the mountain village where I visited last year. 2. I will never forget the days when I spent in the countryside. 3. This is the mountain village （which）I visited last year.
when和while用法区别专项练习 讲解三例句： 1. The girls are dancing while the boys are singing. 2. Kangkang’s mother is cooking when he gets home. 3. When/While Kangkang’s mother is cooking, he gets home. 一、用when或者while填空 Margo was talking on the phone, her sister walked in. we visited the school, the children were playing games. · Sarah was at the barber’s, I was going to class. I saw Carlos, he was wearing a green shirt. Allen was cleaning his room, the phone rang. Rita bought her new dog; it was wearing a little coat. 7. He was driving along ________ suddenly a woman appeared. 8. _____ Jake was waiting at the door, an old woman called to him. 9. He was reading a book ______suddenly the telephone rang. 10. ______ it began to rain, they were playing chess. [ 二、用所给动词适当形式填空 11. While Jake __________ (look) for customers, he _______ (see) a woman. 12. They __________ (play) football on the playground when it _____ (begin) to rain. 13. A strange box ________ (arrive) while we _________ (talk). 14. John ____________ (sleep) when someone __________ (steal) his car. 15. Father still (sleep) when I (get) up yesterday morning. 16. Grandma (cook) breakfast while I (wash) my face this morning. 17. Mother (sweep) the floor when I (leave) home. ~ 18. I (read) a history book when someone (knock ) at the door. 19. Mary and Alice are busy (do) their homework. 20. The teacher asked us (keep) the windows closed. 21. I followed it (see) where it was going. 22. The students (play) basketball on the playground from 3 to 4 yesterday afternoon. / 三、完成下面句子，词数不限 1.飞机在伦敦起飞时正在下雨。 It when the plane in London. 2.你记得汶川大地震时你在做什么吗 Do you remember what you when Wenchuan Earthquake . 3.当铃声想起的时候，我们正在操场上玩得很开心。 We on the playground when the bell . 4.当妈妈下班回家时，你在做什么 % when Mum from work 5.当我在做作业时，有人敲门。 I was doing my homework, someone
过去进行时过去进行时表示过去某一时刻或者某段时间正在进行或发生的动作，常和表过去的时间状语连用，如： 1. I was doing my homework at this time yesterday. 昨天的这个时候我正在做作业。 2. They were waiting for you yesterday. 他们昨天一直在等你。 3. He was cooking in the kitchen at 12 o'clock yesterday. 昨天12点，他正在厨房烧饭。 过去进行时的构成： 肯定形式：主语+was/were+V-ing 否定形式：主语+was not (wasn't)/were not (weren't)+V-ing 疑问形式：Was/Were+主语+V-ing。 基本用法： 1. 过去进行时表示过去某一段时间或某一时刻正在进行的动作。常与之连用的时间状语有，at that time/moment, (at) this time yesterday (last night/Sunday/week…), at+点钟+yesterday (last night / Sunday…)，when sb. did sth.等时间状语从句，如： 1）What were you doing at 7p.m. yesterday? 昨天晚上七点你在干什么？ 2）I first met Mary three years ago. She was working at a radio shop at the time. 我第一次遇到玛丽是在三年前，当时她在一家无线电商店工作。 3）I was cooking when she knocked at the door. 她敲门时我正在做饭。 2. when后通常用表示暂短性动词，while后通常用表示持续性动词，而while所引导的状语从句中，谓语动词常用进行时态，如： When the car exploded I was walking past it. = While I was walking past the car it exploded. 3. when用作并列连词时，主句常用进行时态，从句则用一般过去时，表示主句动作发生的过程中，另一个意想不到的动作发生了。如： I was walking in the street when someone called me. 我正在街上走时突然有人喊我。 4. when作并列连词，表示“（这时）突然”之意时，第一个并列分句用过去进行时，when引导的并列分句用一般过去时。如：
Period____8______ in Unit ___6_________ （Period_____20_____Week 4-6）Subject _Grammar (B) Teaching aims To use the Past Continuous Tense with while and when correctly Teaching interesting points of this class: The differences of when and while in Past Continuous Tense Teaching steps: I. Cooperation and intercourse 1. Check the preparation 2. Discuss in groups II. Question and explanation Check the exercises on page 101, and explain the differences between when and while. when和while的区别是： when只能用于一般时态 while可以用于进行时态 when conj. 在...的时候, 当…的时候 when 在绝大多数情况下，所引导的从句中，应该使用非延续性动词（也叫瞬间动词） 例如：I'll call you when I get there. 我一到那里就给你打电话 I was about to go out when the telephone rang.我刚要出门，电话铃就响了。 但是，when 却可以be 连用。 例如：I lived in this village when I was a boy. 当我还是个孩子的时候我住在这个村庄里。 When I was young, I was sick all the time. 在我小时候我总是生病 while 当...的时候 While he was eating, I asked him to lend me $2. 当他正在吃饭时,我请他借给我二美元。While I read, she sang. 我看书时，她在唱歌。 while 的这种用法一般都和延续性动词连用 while 可以表示“对比‘，这样用有的语法书认为是并列连词 Some people like coffee, while others like tea. 有些人喜欢咖啡, 而有些人喜欢茶。 as 当...之时，一边......一边....... I slipped on the ice as I ran home. 我跑回家时在冰上滑了一跤 He dropped the glass as he stood up. 他站起来时，把杯子摔了。 She sang As she worked. 她一边工作一边唱歌。 III. Rumination and evaluation when, as, while这三个词都可以引出时间状语从句，它们的差别是：when 从句表示某时刻或一段时间as 从句表示进展过程，while 只表示一段时间 When he left the house, I was sitting in the garden. 当他离开家时，我正在院子里坐着。 When he arrived home, it was just nine o'clock.
when 和while的用法区别 ①when是at or during the time that, 既指时间点，也可指一段时间； while是during the time that，只指一段时间，因此when引导的时间状语从句中的动词可以是终止性动词，也可以是延续性动词，而while从句中的动词必须是延续性动词。 ②when 说明从句的动作和主句的动作可以是同时，也可以是先后发生；while 则强调主句的动作在从句动作的发生的过程中或主从句两个动作同时发生。 ③由when引导的时间状语从句，主句用过去进行时，从句应用一般过去时；如果从句和主句的动作同时发生，两句都用过去进行时的时候，多用while引导，如： a. When the teacher came in, we were talking. 当此句改变主从句的位置时，则为： While we were talking, the teacher came in. b. They were singing while we were dancing. ④when和while 还可作并列连词。when表示“在那时”；while表示“而，却”，表对照关系。如： a. The children were running to move the bag of rice when they heard the sound of a motor bike. 孩子们正要跑过去搬开那袋米，这时他们听到了摩托车的声音。 b. He is strong while his brother is weak. 他长得很结实，而他弟弟却很瘦弱。 when，while，as引导时间状语从句的区别 when，while，as显然都可以引导时间状语从句，但用法区别非常大。 一、when可以和延续性动词连用，也可以和短暂性动词连用；而while和as只能和延续性动词连用。 ①Why do you want a new job when youve got such a good one already？（get 为短暂性动词）你已经找到如此好的工作，为何还想再找新的？ ②Sorry，I was out when you called me．（call为短暂性动词）对不起，你打电话时我刚好外出了。 ③Strike while the iron is hot．（is为延续性动词，表示一种持续的状态）趁热打铁。 ④The students took notes as they listened．（listen为延续性动词）学生们边听课边做笔记。 二、when从句的谓语动词可以在主句谓语动作之前、之后或同时发生；while和as从句的谓语动作必须是和主句谓语动作同时发生。 1．从句动作在主句动作前发生，只用when。 ①When he had finished his homework，he took a short rest．（finished先发生）当他完成作业后，他休息了一会儿。 ②When I got to the airport，the guests had left．（got to后发生）当我赶到飞机场时，客人们已经离开了。 2．从句动作和主句动作同时发生，且从句动作为延续性动词时，when，while，as都可使用。
牛津上海版九年级英语下册Unit-
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九下Unit 6 单词短语归纳 １.实施，执行v 1. 承受压力 2．争吵n ２.集中于 ３.集中(注意力、精力等)于v 3.解决,处理 4.压力n 4.deaｌwｉth 5．是否5．担心 6.风险n6．防止，提防7．守卫,保卫 7.抵消,对消 8.取消，撤退，终止8. 忙于做某事 9.有希望的9.把....．抛在后面 10.强迫,迫使(某人做某事）1０.使．.．.．振奋起来 11.音乐会,演奏会１1.学着做,开始做 12．私人的１2.持乐观的态度13.不说话的，沉默的1３. 抵消,对消 1４.危害物，大敌１４．讲笑话 1５.鼓励,鼓舞15.去听音乐会 16.牙科医生 1７.沮丧的，消沉的，无精打采的aｄj 知识点 1.conduct v.实施；执行n_＿_＿___＿__指挥家 Ｃonducｔ a sｕrvey aｂｏｕt a ｈeaｌthy liｆｅstyｌe．____＿____＿___＿_＿_____＿＿___＿_____ cｏnduｃｔ此处用作及物动词,意为“实施，执行”。 我决定去执行一个任务__＿＿________＿______＿___________ 2.quaｒreｌn.争吵 ｈａｖe a ｑuａrrel ｗitｈsｂ．__＿_＿_＿__＿___＿__＿__ ｈavｅ a quaｒrｅl ａbｏｕt sth.______＿_______＿＿_＿__＿＿___＿ 1)他刚刚和他的最好的朋友吵架了______＿__＿____＿＿＿＿__＿__＿_＿______＿___ ２）他们为钱的事情吵架了___＿_＿_＿_____＿_______＿＿___________＿＿_＿ 3.pleasｕｒe 不可数名词，_＿_____＿____＿_＿_ aｄｊ___＿＿＿____ Dｏｉｎｇsｏmetｈiｎｇfor __＿_________＿___．
While和When在过去进行时中两者的区别如下： ①when是at or during the time that, 既指时间点，也可指一段时间；while是during the time that，只指一段时间，因此when引导的时间状语从句中的动词可以是终止性动词，也可以是延续性动词，而while 从句中的动词必须是延续性动词。 ②when 说明从句的动作和主句的动作可以是同时，也可以是先后发生；while 则强调主句的动作在从句动作的发生的过程中或主从句两个动作同时发生。 ③由when引导的时间状语从句，主句用过去进行时，从句应用一般过去时；如果从句和主句的动作同时发生，两句都用过去进行时的时候，多用while引导，如： a. When the teacher came in, we were talking. 当此句改变主从句的位置时，则为： While we were talking, the teacher came in. b. They were singing while we were dancing. ④when和while 还可作并列连词。when表示“在那时”；while表示“而，却”，表对照关系。如： a. The children were running to move the bag of rice when they heard the sound of a motor bike. 孩子们正要跑过去搬开那袋米，这时他们听到了摩托车的声音。 b. He is strong while his brother is weak. 他长得很结实，而他弟弟却很瘦弱。 when; while 当……时候 while能用when代替; 但是when却不一定能用while代替. while+从句, 动作一定会延续 when+延续性动词/瞬间动词; when he arrived
过去进行时 过去进行时表示过去某一时刻或者某段时间正在进行或发生的动作，常和表过去的时间状语连用，如： 1. I was doing my homework at this time yesterday. 昨天的这个时候我正在做作业。 2. They were waiting for you yesterday. 他们昨天一直在等你。 3. He was cooking in the kitchen at 12 o'clock yesterday. 昨天12点，他正在厨房烧饭。 过去进行时的构成： 肯定形式：主语+was/were+V-ing 否定形式：主语+was not (wasn't)/were not (weren't)+V-ing 疑问形式：Was/Were+主语+V-ing。 基本用法： 1. 过去进行时表示过去某一段时间或某一时刻正在进行的动作。常与之连用的时间状语有，at that time/moment, (at) this time yesterday (last night/Sunday/week…), at+点钟+yesterday (last night / Sunday…)，when sb. did sth.等时间状语从句，如： 1）What were you doing at 7p.m. yesterday? 昨天晚上七点你在干什么？ 2）I first met Mary three years ago. She was working at a radio shop at the time. 我第一次遇到玛丽是在三年前，当时她在一家无线电商店工作。 3）I was cooking when she knocked at the door. 她敲门时我正在做饭。 2. when后通常用表示暂短性动词，while后通常用表示持续性动词，而while所引导的状语从句中，谓语动词常用进行时态，如： When the car exploded I was walking past it. = While I was walking past the car it exploded. 3. when用作并列连词时，主句常用进行时态，从句则用一般过去时，表示主句动作发生的过程中，另一个意想不到的动作发生了。如： I was walking in the street when someone called me. 我正在街上走时突然有人喊我。 4. when作并列连词，表示“（这时）突然”之意时，第一个并列分句用过去进行时，when引导的并列分句用一般过去时。如： 1）I was taking a walk when I met him. 我正在散步，突然遇见了他。 2）We were playing outside when it began to rain. 我们正在外边玩，这时下起雨来了。 过去进行时和一般过去时的用法比较： 1）过去进行时表示过去正在进行的动作，而一般过去时则表示一个完整的动作。 例如：They were writing letters to their friends last night. 昨晚他们在写信给他们的朋友。（没有说明信是否写完） They wrote letters to their friends last night.
as when while 的区别和用法 as when while的用法 一、as的意思是“正当……时候”,它既可表示一个具体的时间点，也可以表示一段时间。as可表示主句和从句的动作同时发生或同时持续，即“点点重合”“线线重合”；又可表示一个动作发生在另一个动作的持续过程中，即“点线重合”， 但不能表示两个动作一前一后发生。如果主句和从句的谓语动词都表示持续性的动作，二者均可用进行时，也可以一个用进行时，一个用一般时或者都用一般时。 1、As I got on the bus，he got off. 我上车，他下车。（点点重合）两个动作都是非延续性的 2、He was writing as I was reading. 我看书时，他在写字。（线线重合）两个动作都是延续性的 3、The students were talking as the teacher came in. 老师进来时，学生们正在讲话。（点线重合）前一个动作是延续性的，而后一个动作时非延续性的 二、while的意思是“在……同时（at the same time that )”“在……期间(for as long as, during the time that）”。从while的本身词义来看，它只能表示一段时间，不能表示具体的时间点。在时间上可以是“线线重合”或“点线重合”，但不能表示“点点重合”。例如： 1、He was watching TV while she was cooking. 她做饭时，他在看电视。（线线重合） 2、He was waiting for me while I was working. 我工作的时候，他正等着我。（线线重合） 3、He asked me a question while I was speaking. 我在讲话时，他问了我一个问题。（点线重合）
when和while的用法和区别 while和when都是表示同时，到底句子中是用when还是while主要看从句和主句中所使用的动词是短暂性动作（瞬时动词）还是持续性动作。 1、若主句表示的是一个短暂性的动作，而从句表示的是一个持续性动作时，两者都可用。如：He fell asleep when [while] he was reading. 他看书时睡着了。 I met him when [while] I was taking a walk in the park. 我在公园散步时遇到了他。 2、若主、从句表示两个同时进行的持续性动作，且强调主句表示的动作延续到从句所指的整个时间，通常要用while。如： Don’t talk while you’re eating. 吃饭时不要说话。 I kept silent while he was writing. 在他写的时候，我默不作声。 3、若从句是一个短暂性动作，而主句是一个持续性动作，可以when 但不用while。如：When he came in, I was listening to the radio. 他进来时，我在听收音机。 It was raining hard when we arrived. 我们到达时正下着大雨。 4、若主、从句表示的是两个同时(或几乎同时)发生的短暂性动作，一般要用when。如： I thought of it just when you opened your mouth. 就在你要说的时候，我也想到了。 至于什么是短暂性动作，什么是持续性动作，其实有个很简单的规律。就是如果是进行时态，一般是持续性的。如果是过去式，一般是短暂性动作。 对于，填写when还是while的问题，通常首先看主句和从句中的时态，再根据以上4个规律来判断填写那个单词。 When和While的区别 通常when 后面接一般过去时while 后面接过去进行时 ①when是at or during the time that, 既指时间点，也可指一段时间，while是during the time that，只指一段时间，因此when引导的时间状语从句中的动词可以是终止性动词，也可以是延续性动词，而while从句中的动词必须是延续性动词。 ②when 说明从句的动作和主句的动作可以是同时，也可以是先后发生；while 则强调主句的动作在从句动作的发生的过程中或主从句两个动作同时发生。 ③由when引导的时间状语从句，主句用过去进行时，从句应用一般过去时；如果从句和主句的动作同时发生，两句都用过去进行时的时候，多用while引导，如： a. When the teacher came in, we were talking. 当此句改变主从句的位置时，则为： While we were talking, the teacher came in. b. They were singing while we were dancing. ④when和while 还可作并列连词。when表示“在那时”；while表示“而，却”，表对照关系。如： a. The children were running to move the bag of rice when they heard the sound of a motor bike.
when，while，as引导时间状语从句的区别 when，while，as显然都可以引导时间状语从句，但用法区别非常大。 一、when可以和延续性动词连用，也可以和短暂性动词连用；而while和as只能和延续性动词连用。 ①Why do you want a new job when youve got such a good one already？（get 为短暂性动词）你已经找到如此好的工作，为何还想再找新的？ ②Sorry，I was out when you called me．（call为短暂性动词）对不起，你打电话时我刚好外出了。 ③Strike while the iron is hot．（is为延续性动词，表示一种持续的状态）趁热打铁。 ④The students took notes as they listened．（listen为延续性动词）学生们边听课边做笔记。 二、when从句的谓语动词可以在主句谓语动作之前、之后或同时发生；while 和as从句的谓语动作必须是和主句谓语动作同时发生。 1．从句动作在主句动作前发生，只用when。 ①When he had finished his homework，he took a short rest．（finished先发生）当他完成作业后，他休息了一会儿。 ②When I got to the airport，the guests had left．（got to后发生）当我赶到飞机场时，客人们已经离开了。 2．从句动作和主句动作同时发生，且从句动作为延续性动词时，when，while，as都可使用。 ①When ／While ／As we were dancing，a stranger came in．（dance为延续性动词）当我们跳舞时，一位陌生人走了进来。 ②When ／While ／As she was making a phonecall，I was writing a letter．（make为延续性动词）当她在打电话时，我正在写信。 3．当主句、从句动作同时进行，从句动作的时间概念淡化，而主要表示主句动作发生的背景或条件时，只能用as。这时，as常表示“随着……”；“一边……，一边……”之意。 ①As the time went on，the weather got worse．（as表示“随着……”之意） ②The atmosphere gets thinner and thinner as the height increases．随着高度的增加，大气越来越稀薄。 ③As years go by，China is getting stronger and richer．随着时间一年一年过去，中国变得越来越富强了。 ④The little girls sang as they went．小姑娘们一边走，一边唱。 ⑤The sad mother sat on the roadside，shouting as she was crying．伤心的妈妈坐在路边，边哭边叫。 4．在将来时从句中，常用when，且从句须用一般时代替将来时。 ①You shall borrow the book when I have finished reading it．在我读完这本书后，你可以借阅。 ②When the manager comes here for a visit next week，Ill talk with him about this．下周，经理来这参观时，我会和他谈谈此事。 三、when用于表示“一……就……”的句型中（指过去的事情）。 sb．had hardly（＝scarcely）done sth．when．．．＝Hardly ／Scarcely had sb．done sth．when．．．
一、从句是如何出题的 1. 时态 2. 考连接词 3. 考语言顺序 二、学好从句的两个基本条件 1. 时态 2. 从句的三个必须:①必须是句子；②必须有连接词；③必须是陈述句 三、状语从句、宾语从句、定语从句重点 1.如何判断何种从句 2. 从句的时态 3. 从句的连接词与扩展 4. 经典单选、从句与选词、长句子分析 四、如何判断三种从句 1. 状语从句无先行词 2. 宾语(表语)从句无先行词有动词或词组 3. 定语从句先行词多为名词或代词 一、When引导的定语从句与时间状语从句的区分 1. when的译法不同。在时间状语中，when 翻译成“当……的时候”I want to be a teacher when I grow up. 当我长大的时候，我要做一名老师。在定语从句中，when不翻译。I won't forget the day when he says he loves me. 我不会忘记他说爱我的那一天。 2. 在时间状语中，when从句前面或后面是句子；定语从句中，when从句不能位于句首，且通常when前为表示时间的名词day、year等。 3. when在从句的作用不同。在时间状语从句中，when是连词，只起连接主句和从句的作用，不做从句的任何成分。不过when引导的时间状语从句修饰主句的谓语，做主句的时间状语。 在定语从句中，when是关系副词，在从句中代替先行词做从句的时间状语，修饰从句的谓语。 例1 I will always remember the days when I lived with my grandparents in the country. 例2 I always remember the days in the country when I see the photo of my grandparents. 点评：例1意为“我会永远记得跟我祖父母一起住在乡下的那些日子”，其中when 引导的是一个定语从句, 修饰the days, when在从句中作时间状语。例2意为“当我看到祖父母的照片时，总是会想起在
w h e n和w h i l e区别
0when和while的区别 ①when是at or during the time that, 既指时间点，也可指一段时间； while是during the time that，只指一段时间，因此when引导的时间状语从句中的动词可以是终止性动词，也可以是延续性动词，而while从句中的动词必须是延续性动词。 ②when 说明从句的动作和主句的动作可以是同时，也可以是先后发生；while 则强调主句的动作在从句动作的发生的过程中或主从句两个动作同时发生。 ③由when引导的时间状语从句，主句用过去进行时，从句应用一般过去时；如果从句和主句的动作同时发生，两句都用过去进行时的时候，多用while引导，如： a. When the teacher came in, we were talking. 当此句改变主从句的位置时，则为： While we were talking, the teacher came in. b. They were singing while we were dancing. ④when和while 还可作并列连词。when表示“在那时”；while表示“而，却”，表对照关系。如： a. The children were running to move the bag of rice when they heard the sound of a motor bike.
孩子们正要跑过去搬开那袋米，这时他们听到了摩托车的声音。 b. He is strong while his brother is weak. 他长得很结实，而他弟弟却很瘦弱。 具体你可以参考这一段。 when，while，as引导时间状语从句的区别 when，while，as显然都可以引导时间状语从句，但用法区别非常大。 一、when可以和延续性动词连用，也可以和短暂性动词连用；而while和as只能和延续性动词连用。 ① Why do you want a new job when youve got such a good one already？（get为短暂性动词）你已经找到如此好的工作，为何还想再找新的？ ②Sorry，I was out when you called me．（call为短暂性动词）对不起，你打电话时我刚好外出了。 ③Strike while the iron is hot．（is为延续性动词，表示一种持续的状态）趁热打铁。 ④ The students took notes as they listened．（listen为延续性动词）学生们边听课边做笔记。